Abstract:
Fraud has become a trillion-dollar industry today. Some finance companies have separate
domain expert teams and data scientists are working on identifying fraudulent activities. Data
Scientists often use complex statistical models to identify frauds. However, there are many
disadvantages to this approach. Fraud detection is not real-time and therefore, in many cases
fraudulent activities are identified only after the actual fraud has happened. These methods are
prone to human errors. In addition, it requires expensive, highly skilled domain expert teams and
data scientists. Nevertheless, the accuracy of manual fraud detection mythologies is low and it is
very difficult to handle large volumes of data. More often, it requires time-consuming
investigations into the other transactions related to the fraudulent activity in order to identify
fraudulent activity patterns. Finance companies are not getting adequate return of interest (ROI)
despite the resources and money spent on these traditional methods. Most of the traditional
fraud detection models focused on discrete data points. (User accounts, IP addresses devices,
etc…) However, these methods are no longer sufficient for today’s needs. As fraudsters and
hackers are using more advance and cutting edge techniques to mask their fraudulent activities
even from the sharpest eyes. These methods can only detect known types of attacks therefore an
analytical approach is required to address these drawbacks of the traditional methods.
In order to address the above-mentioned issues in fraud detection domain, a new fraud detection
system was introduced which uses an Artificial Neural Network to identify fraudulent
transactions.
